Drum and Melody Generation using LSTM - based Neural Networks

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dc.contributor.advisorGuclu, Umut
dc.contributor.advisorAmbrogioni, Luca
dc.contributor.authorBeissel, Clemens Carl Christopher
dc.date.issued2019-02-12
dc.description.abstractFor this project, I constructed two LSTM - based neural networks that can generate monophonic melodies and polyphonic drum patterns. As opposed to projects which were conducted in the past, this attempt was focused on a combination of genres rather than training on only one instrument from one genre. When generating melodies, the patterns that resulted from this challenge were somewhat chaotic with some potentially inspirational exceptions. Generated drums, on the other hand, oftentimes converged to an "average of all genres" when no controlled randomness was introduced (section 7.1.5). A better imitation of the training data can certainly be achieved by using only one genre. But the involvement of several different genres led to a more unpredictable and creative outcome.en_US
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/10881
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeBacheloren_US
dc.titleDrum and Melody Generation using LSTM - based Neural Networksen_US
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